Overview

Dataset statistics

Number of variables16
Number of observations7295
Missing cells766
Missing cells (%)0.7%
Duplicate rows6144
Duplicate rows (%)84.2%
Total size in memory833.8 KiB
Average record size in memory117.0 B

Variable types

Numeric10
DateTime1
Categorical5

Alerts

Dataset has 6144 (84.2%) duplicate rowsDuplicates
Customer_name has a high cardinality: 214 distinct valuesHigh cardinality
Customer_address has a high cardinality: 131 distinct valuesHigh cardinality
No is highly overall correlated with Orignal_priceHigh correlation
Item_code is highly overall correlated with Discount_price and 3 other fieldsHigh correlation
Orignal_price is highly overall correlated with No and 4 other fieldsHigh correlation
PCS is highly overall correlated with Discount_priceHigh correlation
Total_discount is highly overall correlated with Orignal_price and 2 other fieldsHigh correlation
Discount_price is highly overall correlated with Item_code and 3 other fieldsHigh correlation
Discount_per_piece is highly overall correlated with Orignal_price and 4 other fieldsHigh correlation
Product_name is highly overall correlated with Item_code and 2 other fieldsHigh correlation
Price Range is highly overall correlated with Item_code and 4 other fieldsHigh correlation
Has_Discount is highly overall correlated with Item_code and 4 other fieldsHigh correlation
Price Range has 766 (10.5%) missing valuesMissing
PCS is highly skewed (γ1 = 28.05350099)Skewed
Total_discount is highly skewed (γ1 = 23.54544337)Skewed
CTN has 6983 (95.7%) zerosZeros
Total_discount has 4855 (66.6%) zerosZeros
Discount_per_piece has 4855 (66.6%) zerosZeros

Reproduction

Analysis started2023-05-12 07:11:24.441138
Analysis finished2023-05-12 07:11:45.193856
Duration20.75 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

No
Real number (ℝ)

Distinct3313
Distinct (%)45.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1663.8337
Minimum3
Maximum3345
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.1 KiB
2023-05-12T12:11:45.431864image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile148
Q1815.5
median1679
Q32507.5
95-th percentile3183.3
Maximum3345
Range3342
Interquartile range (IQR)1692

Descriptive statistics

Standard deviation974.33919
Coefficient of variation (CV)0.5855989
Kurtosis-1.1980502
Mean1663.8337
Median Absolute Deviation (MAD)846
Skewness-0.00077986273
Sum12137667
Variance949336.86
MonotonicityNot monotonic
2023-05-12T12:11:45.630945image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3174 13
 
0.2%
3225 13
 
0.2%
184 9
 
0.1%
114 9
 
0.1%
2142 9
 
0.1%
440 8
 
0.1%
16 8
 
0.1%
778 7
 
0.1%
3083 7
 
0.1%
3304 7
 
0.1%
Other values (3303) 7205
98.8%
ValueCountFrequency (%)
3 2
< 0.1%
4 2
< 0.1%
5 2
< 0.1%
6 4
0.1%
7 3
< 0.1%
8 3
< 0.1%
9 1
 
< 0.1%
10 2
< 0.1%
11 3
< 0.1%
12 3
< 0.1%
ValueCountFrequency (%)
3345 1
 
< 0.1%
3344 1
 
< 0.1%
3343 2
< 0.1%
3342 2
< 0.1%
3341 2
< 0.1%
3340 1
 
< 0.1%
3339 1
 
< 0.1%
3338 3
< 0.1%
3337 1
 
< 0.1%
3336 2
< 0.1%

Date
Date

Distinct373
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Memory size57.1 KiB
Minimum2021-09-10 00:00:00
Maximum2022-12-03 00:00:00
2023-05-12T12:11:45.863325image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:46.084322image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

C/code
Real number (ℝ)

Distinct221
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5187.6692
Minimum2005
Maximum9965
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.1 KiB
2023-05-12T12:11:46.392820image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum2005
5-th percentile2023
Q12136
median3403
Q39024
95-th percentile9921
Maximum9965
Range7960
Interquartile range (IQR)6888

Descriptive statistics

Standard deviation3349.1661
Coefficient of variation (CV)0.64560132
Kurtosis-1.7649357
Mean5187.6692
Median Absolute Deviation (MAD)1380
Skewness0.33722082
Sum37844047
Variance11216913
MonotonicityNot monotonic
2023-05-12T12:11:46.604995image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8992 474
 
6.5%
2159 254
 
3.5%
9024 192
 
2.6%
2023 177
 
2.4%
2118 155
 
2.1%
2069 149
 
2.0%
6511 140
 
1.9%
6544 131
 
1.8%
2274 125
 
1.7%
9451 124
 
1.7%
Other values (211) 5374
73.7%
ValueCountFrequency (%)
2005 19
 
0.3%
2007 71
1.0%
2009 75
1.0%
2011 10
 
0.1%
2013 4
 
0.1%
2014 52
 
0.7%
2015 25
 
0.3%
2019 71
1.0%
2022 3
 
< 0.1%
2023 177
2.4%
ValueCountFrequency (%)
9965 66
0.9%
9964 16
 
0.2%
9953 44
0.6%
9952 26
 
0.4%
9947 100
1.4%
9935 1
 
< 0.1%
9931 5
 
0.1%
9929 46
0.6%
9927 55
0.8%
9921 22
 
0.3%

Customer_name
Categorical

Distinct214
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size57.1 KiB
PIZZA FLAME
 
474
SNACK FOOD CAFE
 
254
AL MAIDA 2
 
192
ARYANA S/S
 
177
TOP TOWN
 
155
Other values (209)
6043 

Length

Max length30
Median length22
Mean length13.428239
Min length4

Characters and Unicode

Total characters97959
Distinct characters53
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)0.2%

Sample

1st rowBIG BITE FAST FOOD
2nd rowBIG BITE FAST FOOD
3rd rowPIZZA FLAME
4th rowPIZZA FLAME
5th rowCHIL & GRILL 0312.9553655

Common Values

ValueCountFrequency (%)
PIZZA FLAME 474
 
6.5%
SNACK FOOD CAFE 254
 
3.5%
AL MAIDA 2 192
 
2.6%
ARYANA S/S 177
 
2.4%
TOP TOWN 155
 
2.1%
CHIEF BURGER 149
 
2.0%
USMAN GENERAL STORE 140
 
1.9%
FOOD DISTRICT 133
 
1.8%
CHATRAL 131
 
1.8%
KHAWAJA GENERAL STORE 125
 
1.7%
Other values (204) 5365
73.5%

Length

2023-05-12T12:11:46.826166image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
store 1715
 
9.7%
g/s 1031
 
5.8%
general 776
 
4.4%
super 702
 
4.0%
food 538
 
3.0%
pizza 476
 
2.7%
flame 474
 
2.7%
mart 432
 
2.4%
bakers 383
 
2.2%
al 352
 
2.0%
Other values (269) 10861
61.2%

Most occurring characters

ValueCountFrequency (%)
A 11776
 
12.0%
10538
 
10.8%
E 7835
 
8.0%
S 7794
 
8.0%
R 7623
 
7.8%
O 4752
 
4.9%
T 4650
 
4.7%
I 4442
 
4.5%
N 3936
 
4.0%
L 3646
 
3.7%
Other values (43) 30967
31.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 81715
83.4%
Space Separator 10538
 
10.8%
Decimal Number 3477
 
3.5%
Other Punctuation 2037
 
2.1%
Lowercase Letter 168
 
0.2%
Open Punctuation 12
 
< 0.1%
Close Punctuation 12
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 11776
14.4%
E 7835
 
9.6%
S 7794
 
9.5%
R 7623
 
9.3%
O 4752
 
5.8%
T 4650
 
5.7%
I 4442
 
5.4%
N 3936
 
4.8%
L 3646
 
4.5%
M 2790
 
3.4%
Other values (16) 22471
27.5%
Decimal Number
ValueCountFrequency (%)
2 667
19.2%
3 621
17.9%
5 608
17.5%
9 430
12.4%
0 328
9.4%
1 275
7.9%
6 234
 
6.7%
4 158
 
4.5%
7 133
 
3.8%
8 23
 
0.7%
Lowercase Letter
ValueCountFrequency (%)
t 36
21.4%
r 24
14.3%
e 24
14.3%
m 12
 
7.1%
a 12
 
7.1%
k 12
 
7.1%
y 12
 
7.1%
v 12
 
7.1%
d 12
 
7.1%
p 12
 
7.1%
Other Punctuation
ValueCountFrequency (%)
/ 1348
66.2%
& 316
 
15.5%
. 210
 
10.3%
' 163
 
8.0%
Space Separator
ValueCountFrequency (%)
10538
100.0%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 81883
83.6%
Common 16076
 
16.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 11776
14.4%
E 7835
 
9.6%
S 7794
 
9.5%
R 7623
 
9.3%
O 4752
 
5.8%
T 4650
 
5.7%
I 4442
 
5.4%
N 3936
 
4.8%
L 3646
 
4.5%
M 2790
 
3.4%
Other values (26) 22639
27.6%
Common
ValueCountFrequency (%)
10538
65.6%
/ 1348
 
8.4%
2 667
 
4.1%
3 621
 
3.9%
5 608
 
3.8%
9 430
 
2.7%
0 328
 
2.0%
& 316
 
2.0%
1 275
 
1.7%
6 234
 
1.5%
Other values (7) 711
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 97959
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 11776
 
12.0%
10538
 
10.8%
E 7835
 
8.0%
S 7794
 
8.0%
R 7623
 
7.8%
O 4752
 
4.9%
T 4650
 
4.7%
I 4442
 
4.5%
N 3936
 
4.0%
L 3646
 
3.7%
Other values (43) 30967
31.6%

Customer_address
Categorical

Distinct131
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size57.1 KiB
IBRAHIM MARKET
 
498
ZANGAL MARKET
 
298
FAQERA ABAD
 
274
OLD BARA ROAD
 
251
SADAR
 
251
Other values (126)
5723 

Length

Max length27
Median length22
Mean length12.910761
Min length3

Characters and Unicode

Total characters94184
Distinct characters56
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowWARSAK ROAD
2nd rowWARSAK ROAD
3rd rowIBRAHIM MARKET
4th rowIBRAHIM MARKET
5th rowMOHMAD ZAI

Common Values

ValueCountFrequency (%)
IBRAHIM MARKET 498
 
6.8%
ZANGAL MARKET 298
 
4.1%
FAQERA ABAD 274
 
3.8%
OLD BARA ROAD 251
 
3.4%
SADAR 251
 
3.4%
University Town 201
 
2.8%
PHASE 4 195
 
2.7%
CAMPUS 185
 
2.5%
Nawab Market 177
 
2.4%
UNIVERSITY ROAD 172
 
2.4%
Other values (121) 4793
65.7%

Length

2023-05-12T12:11:47.085211image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
market 3143
 
20.1%
road 1075
 
6.9%
university 508
 
3.3%
ibrahim 498
 
3.2%
town 369
 
2.4%
nawab 342
 
2.2%
bara 303
 
1.9%
zangal 298
 
1.9%
zai 294
 
1.9%
abad 287
 
1.8%
Other values (141) 8490
54.4%

Most occurring characters

ValueCountFrequency (%)
A 14880
15.8%
8357
 
8.9%
R 8222
 
8.7%
E 5170
 
5.5%
M 4657
 
4.9%
T 4540
 
4.8%
I 3958
 
4.2%
D 3282
 
3.5%
K 3204
 
3.4%
B 3142
 
3.3%
Other values (46) 34772
36.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 73669
78.2%
Lowercase Letter 11585
 
12.3%
Space Separator 8357
 
8.9%
Decimal Number 542
 
0.6%
Other Punctuation 31
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 14880
20.2%
R 8222
11.2%
E 5170
 
7.0%
M 4657
 
6.3%
T 4540
 
6.2%
I 3958
 
5.4%
D 3282
 
4.5%
K 3204
 
4.3%
B 3142
 
4.3%
S 2978
 
4.0%
Other values (15) 19636
26.7%
Lowercase Letter
ValueCountFrequency (%)
a 2302
19.9%
e 1214
10.5%
r 1133
9.8%
t 1019
8.8%
k 825
 
7.1%
i 675
 
5.8%
s 672
 
5.8%
n 655
 
5.7%
o 564
 
4.9%
w 483
 
4.2%
Other values (12) 2043
17.6%
Decimal Number
ValueCountFrequency (%)
4 197
36.3%
2 189
34.9%
6 137
25.3%
3 8
 
1.5%
5 8
 
1.5%
7 3
 
0.6%
Other Punctuation
ValueCountFrequency (%)
; 29
93.5%
, 2
 
6.5%
Space Separator
ValueCountFrequency (%)
8357
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 85254
90.5%
Common 8930
 
9.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 14880
17.5%
R 8222
 
9.6%
E 5170
 
6.1%
M 4657
 
5.5%
T 4540
 
5.3%
I 3958
 
4.6%
D 3282
 
3.8%
K 3204
 
3.8%
B 3142
 
3.7%
S 2978
 
3.5%
Other values (37) 31221
36.6%
Common
ValueCountFrequency (%)
8357
93.6%
4 197
 
2.2%
2 189
 
2.1%
6 137
 
1.5%
; 29
 
0.3%
3 8
 
0.1%
5 8
 
0.1%
7 3
 
< 0.1%
, 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 94184
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 14880
15.8%
8357
 
8.9%
R 8222
 
8.7%
E 5170
 
5.5%
M 4657
 
4.9%
T 4540
 
4.8%
I 3958
 
4.2%
D 3282
 
3.5%
K 3204
 
3.4%
B 3142
 
3.3%
Other values (46) 34772
36.9%

Item_code
Real number (ℝ)

Distinct14
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1764.2591
Minimum1758
Maximum1788
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.1 KiB
2023-05-12T12:11:47.234437image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum1758
5-th percentile1758
Q11760
median1763
Q31770
95-th percentile1772
Maximum1788
Range30
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.414342
Coefficient of variation (CV)0.0030689042
Kurtosis0.32474347
Mean1764.2591
Median Absolute Deviation (MAD)5
Skewness0.80514149
Sum12870270
Variance29.3151
MonotonicityNot monotonic
2023-05-12T12:11:47.367178image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1760 1392
19.1%
1763 1253
17.2%
1758 1173
16.1%
1770 1150
15.8%
1771 997
13.7%
1762 854
11.7%
1772 288
 
3.9%
1761 59
 
0.8%
1784 59
 
0.8%
1759 47
 
0.6%
Other values (4) 23
 
0.3%
ValueCountFrequency (%)
1758 1173
16.1%
1759 47
 
0.6%
1760 1392
19.1%
1761 59
 
0.8%
1762 854
11.7%
1763 1253
17.2%
1770 1150
15.8%
1771 997
13.7%
1772 288
 
3.9%
1784 59
 
0.8%
ValueCountFrequency (%)
1788 3
 
< 0.1%
1787 2
 
< 0.1%
1786 10
 
0.1%
1785 8
 
0.1%
1784 59
 
0.8%
1772 288
 
3.9%
1771 997
13.7%
1770 1150
15.8%
1763 1253
17.2%
1762 854
11.7%

Product_name
Categorical

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size57.1 KiB
DEEN'S MOZARELLA 200GM
1392 
DEEN'S SLICE CHEESE 200GM
1253 
DEEN'S CHEDDAR 200GM
1173 
DEEN'S CHADDER CHEESE BLOCK 2K
1150 
DEEN'S MOZRELLA CHEESE BLOCK
997 
Other values (11)
1330 

Length

Max length30
Median length28
Mean length24.57889
Min length16

Characters and Unicode

Total characters179303
Distinct characters30
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowDEEN'S CHADDER CHEESE BLOCK 2K
2nd rowDEEN'S MOZRELLA CHEESE BLOCK
3rd rowDEEN'S CHADDER CHEESE BLOCK 2K
4th rowDEEN'S MOZRELLA CHEESE BLOCK
5th rowDEEN'S CHADDER CHEESE BLOCK 2K

Common Values

ValueCountFrequency (%)
DEEN'S MOZARELLA 200GM 1392
19.1%
DEEN'S SLICE CHEESE 200GM 1253
17.2%
DEEN'S CHEDDAR 200GM 1173
16.1%
DEEN'S CHADDER CHEESE BLOCK 2K 1150
15.8%
DEEN'S MOZRELLA CHEESE BLOCK 997
13.7%
DEEN'S PIZZA CHEESE 200GM 854
11.7%
DEEN SLICE CHEESE 1 KG 192
 
2.6%
DEEN'S SLICE CHEESE 1 KG 95
 
1.3%
DEEN'S MOZARELLA 400GM 59
 
0.8%
DEENS SLICE 250G 59
 
0.8%
Other values (6) 71
 
1.0%

Length

2023-05-12T12:11:47.535452image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
deen's 7020
25.2%
200gm 4672
16.8%
cheese 4542
16.3%
block 2147
 
7.7%
slice 1600
 
5.7%
mozarella 1451
 
5.2%
cheddar 1220
 
4.4%
chadder 1160
 
4.2%
2k 1150
 
4.1%
mozrella 1007
 
3.6%
Other values (11) 1896
 
6.8%

Most occurring characters

ValueCountFrequency (%)
E 34652
19.3%
20570
11.5%
S 13244
 
7.4%
D 12055
 
6.7%
C 10669
 
6.0%
0 9641
 
5.4%
L 8663
 
4.8%
N 7294
 
4.1%
M 7236
 
4.0%
A 7146
 
4.0%
Other values (20) 48133
26.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 135690
75.7%
Space Separator 20570
 
11.5%
Decimal Number 16019
 
8.9%
Other Punctuation 7020
 
3.9%
Lowercase Letter 3
 
< 0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 34652
25.5%
S 13244
 
9.8%
D 12055
 
8.9%
C 10669
 
7.9%
L 8663
 
6.4%
N 7294
 
5.4%
M 7236
 
5.3%
A 7146
 
5.3%
H 6922
 
5.1%
G 5148
 
3.8%
Other values (7) 22661
16.7%
Decimal Number
ValueCountFrequency (%)
0 9641
60.2%
2 5881
36.7%
1 306
 
1.9%
4 128
 
0.8%
5 59
 
0.4%
9 2
 
< 0.1%
7 2
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
w 1
33.3%
y 1
33.3%
h 1
33.3%
Space Separator
ValueCountFrequency (%)
20570
100.0%
Other Punctuation
ValueCountFrequency (%)
' 7020
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 135693
75.7%
Common 43610
 
24.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 34652
25.5%
S 13244
 
9.8%
D 12055
 
8.9%
C 10669
 
7.9%
L 8663
 
6.4%
N 7294
 
5.4%
M 7236
 
5.3%
A 7146
 
5.3%
H 6922
 
5.1%
G 5148
 
3.8%
Other values (10) 22664
16.7%
Common
ValueCountFrequency (%)
20570
47.2%
0 9641
22.1%
' 7020
 
16.1%
2 5881
 
13.5%
1 306
 
0.7%
4 128
 
0.3%
5 59
 
0.1%
9 2
 
< 0.1%
7 2
 
< 0.1%
- 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 179303
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 34652
19.3%
20570
11.5%
S 13244
 
7.4%
D 12055
 
6.7%
C 10669
 
6.0%
0 9641
 
5.4%
L 8663
 
4.8%
N 7294
 
4.1%
M 7236
 
4.0%
A 7146
 
4.0%
Other values (20) 48133
26.8%

Orignal_price
Real number (ℝ)

Distinct43
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23850.73
Minimum14400
Maximum36006
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.1 KiB
2023-05-12T12:11:47.724559image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum14400
5-th percentile15840
Q121180
median23725
Q326650
95-th percentile31460
Maximum36006
Range21606
Interquartile range (IQR)5470

Descriptive statistics

Standard deviation4820.6164
Coefficient of variation (CV)0.20211609
Kurtosis-0.59108625
Mean23850.73
Median Absolute Deviation (MAD)2925
Skewness-0.21520351
Sum1.7399107 × 108
Variance23238342
MonotonicityNot monotonic
2023-05-12T12:11:47.913075image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
15900 549
 
7.5%
21180 488
 
6.7%
29315 381
 
5.2%
30880 379
 
5.2%
22425 356
 
4.9%
23600 350
 
4.8%
26650 335
 
4.6%
22550 313
 
4.3%
26390 312
 
4.3%
20150 305
 
4.2%
Other values (33) 3527
48.3%
ValueCountFrequency (%)
14400 86
 
1.2%
14500 236
3.2%
15840 133
 
1.8%
15900 549
7.5%
16900 52
 
0.7%
17600 69
 
0.9%
19250 211
 
2.9%
20150 305
4.2%
20700 21
 
0.3%
21180 488
6.7%
ValueCountFrequency (%)
36006 10
 
0.1%
34848 56
 
0.8%
33660 3
 
< 0.1%
32472 80
 
1.1%
31460 217
3.0%
31314 8
 
0.1%
30880 379
5.2%
30600 13
 
0.2%
29315 381
5.2%
29232 65
 
0.9%

CTN
Real number (ℝ)

Distinct11
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.14201508
Minimum0
Maximum12
Zeros6983
Zeros (%)95.7%
Negative0
Negative (%)0.0%
Memory size57.1 KiB
2023-05-12T12:11:48.060002image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum12
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.87440468
Coefficient of variation (CV)6.1571256
Kurtosis70.343437
Mean0.14201508
Median Absolute Deviation (MAD)0
Skewness7.9089773
Sum1036
Variance0.76458355
MonotonicityNot monotonic
2023-05-12T12:11:48.210356image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 6983
95.7%
1 126
 
1.7%
5 43
 
0.6%
2 41
 
0.6%
4 27
 
0.4%
10 20
 
0.3%
3 19
 
0.3%
6 16
 
0.2%
7 12
 
0.2%
8 7
 
0.1%
ValueCountFrequency (%)
0 6983
95.7%
1 126
 
1.7%
2 41
 
0.6%
3 19
 
0.3%
4 27
 
0.4%
5 43
 
0.6%
6 16
 
0.2%
7 12
 
0.2%
8 7
 
0.1%
10 20
 
0.3%
ValueCountFrequency (%)
12 1
 
< 0.1%
10 20
 
0.3%
8 7
 
0.1%
7 12
 
0.2%
6 16
 
0.2%
5 43
 
0.6%
4 27
 
0.4%
3 19
 
0.3%
2 41
 
0.6%
1 126
1.7%

PCS
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct36
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0138451
Minimum1
Maximum714
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.1 KiB
2023-05-12T12:11:48.385970image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile12
Maximum714
Range713
Interquartile range (IQR)2

Descriptive statistics

Standard deviation15.520357
Coefficient of variation (CV)3.0955
Kurtosis1202.9283
Mean5.0138451
Median Absolute Deviation (MAD)1
Skewness28.053501
Sum36576
Variance240.88149
MonotonicityNot monotonic
2023-05-12T12:11:48.735916image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
2 2212
30.3%
3 1679
23.0%
1 1185
16.2%
6 800
 
11.0%
4 700
 
9.6%
10 128
 
1.8%
5 128
 
1.8%
12 93
 
1.3%
8 79
 
1.1%
50 45
 
0.6%
Other values (26) 246
 
3.4%
ValueCountFrequency (%)
1 1185
16.2%
2 2212
30.3%
3 1679
23.0%
4 700
 
9.6%
5 128
 
1.8%
6 800
 
11.0%
7 8
 
0.1%
8 79
 
1.1%
9 1
 
< 0.1%
10 128
 
1.8%
ValueCountFrequency (%)
714 2
 
< 0.1%
160 1
 
< 0.1%
144 2
 
< 0.1%
120 1
 
< 0.1%
102 2
 
< 0.1%
100 20
0.3%
80 11
0.2%
72 12
0.2%
70 12
0.2%
66 2
 
< 0.1%

Total_discount
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct341
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean199.38944
Minimum-120
Maximum53125
Zeros4855
Zeros (%)66.6%
Negative2
Negative (%)< 0.1%
Memory size57.1 KiB
2023-05-12T12:11:48.940564image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum-120
5-th percentile0
Q10
median0
Q390
95-th percentile480
Maximum53125
Range53245
Interquartile range (IQR)90

Descriptive statistics

Standard deviation1275.6995
Coefficient of variation (CV)6.3980292
Kurtosis850.37653
Mean199.38944
Median Absolute Deviation (MAD)0
Skewness23.545443
Sum1454546
Variance1627409.2
MonotonicityNot monotonic
2023-05-12T12:11:49.148273image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4855
66.6%
180 175
 
2.4%
200 134
 
1.8%
90 115
 
1.6%
150 108
 
1.5%
270 93
 
1.3%
236 88
 
1.2%
30 69
 
0.9%
88 69
 
0.9%
354 62
 
0.8%
Other values (331) 1527
 
20.9%
ValueCountFrequency (%)
-120 1
 
< 0.1%
-50 1
 
< 0.1%
0 4855
66.6%
7 2
 
< 0.1%
8 3
 
< 0.1%
9 2
 
< 0.1%
11 1
 
< 0.1%
14 2
 
< 0.1%
15 2
 
< 0.1%
16 1
 
< 0.1%
ValueCountFrequency (%)
53125 2
< 0.1%
24220 1
 
< 0.1%
14000 2
< 0.1%
13080 1
 
< 0.1%
12000 3
< 0.1%
11800 2
< 0.1%
11600 1
 
< 0.1%
11400 1
 
< 0.1%
11124 1
 
< 0.1%
10900 2
< 0.1%

Discount_price
Real number (ℝ)

Distinct741
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4995.5707
Minimum-26735
Maximum258600
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)< 0.1%
Memory size57.1 KiB
2023-05-12T12:11:49.343290image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum-26735
5-th percentile406
Q1902
median1500
Q32904
95-th percentile11600
Maximum258600
Range285335
Interquartile range (IQR)2002

Descriptive statistics

Standard deviation17722.557
Coefficient of variation (CV)3.547654
Kurtosis74.995887
Mean4995.5707
Median Absolute Deviation (MAD)770
Skewness8.0410524
Sum36442689
Variance3.1408901 × 108
MonotonicityNot monotonic
2023-05-12T12:11:49.527313image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3000 148
 
2.0%
2700 147
 
2.0%
812 126
 
1.7%
1353 125
 
1.7%
902 121
 
1.7%
3700 111
 
1.5%
2255 110
 
1.5%
690 105
 
1.4%
1158 101
 
1.4%
1218 93
 
1.3%
Other values (731) 6108
83.7%
ValueCountFrequency (%)
-26735 1
< 0.1%
-21811 1
< 0.1%
2 1
< 0.1%
224 1
< 0.1%
247 1
< 0.1%
264 1
< 0.1%
278 1
< 0.1%
281 2
< 0.1%
282 1
< 0.1%
287 1
< 0.1%
ValueCountFrequency (%)
258600 1
 
< 0.1%
225500 7
0.1%
215500 5
0.1%
201280 1
 
< 0.1%
200000 2
 
< 0.1%
180400 4
0.1%
172400 1
 
< 0.1%
160000 1
 
< 0.1%
157850 5
0.1%
153600 2
 
< 0.1%

Discount_per_piece
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct262
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.963851
Minimum-60
Maximum901
Zeros4855
Zeros (%)66.6%
Negative2
Negative (%)< 0.1%
Memory size57.1 KiB
2023-05-12T12:11:49.730013image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum-60
5-th percentile0
Q10
median0
Q324.166667
95-th percentile118
Maximum901
Range961
Interquartile range (IQR)24.166667

Descriptive statistics

Standard deviation53.777863
Coefficient of variation (CV)1.994443
Kurtosis31.924342
Mean26.963851
Median Absolute Deviation (MAD)0
Skewness3.803901
Sum196701.29
Variance2892.0586
MonotonicityNot monotonic
2023-05-12T12:11:49.921368image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4855
66.6%
90 430
 
5.9%
100 244
 
3.3%
118 238
 
3.3%
75 179
 
2.5%
88 151
 
2.1%
30 128
 
1.8%
140 72
 
1.0%
218 53
 
0.7%
17 38
 
0.5%
Other values (252) 907
 
12.4%
ValueCountFrequency (%)
-60 1
 
< 0.1%
-50 1
 
< 0.1%
0 4855
66.6%
4 1
 
< 0.1%
7 2
 
< 0.1%
7.5 1
 
< 0.1%
8 3
 
< 0.1%
8.333333333 1
 
< 0.1%
8.5 2
 
< 0.1%
8.666666667 4
 
0.1%
ValueCountFrequency (%)
901 1
< 0.1%
822 1
< 0.1%
817.3076923 1
< 0.1%
620.5 1
< 0.1%
564.6 1
< 0.1%
556.2 1
< 0.1%
520.8333333 1
< 0.1%
426.5 1
< 0.1%
409 1
< 0.1%
358 2
< 0.1%

Month
Real number (ℝ)

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.9039068
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.1 KiB
2023-05-12T12:11:50.085381image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.543332
Coefficient of variation (CV)0.51323578
Kurtosis-1.3850468
Mean6.9039068
Median Absolute Deviation (MAD)3
Skewness-0.1723748
Sum50364
Variance12.555201
MonotonicityNot monotonic
2023-05-12T12:11:50.223392image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
11 994
13.6%
10 869
11.9%
9 824
11.3%
4 756
10.4%
3 660
9.0%
2 581
8.0%
12 524
7.2%
1 476
6.5%
6 431
5.9%
5 420
5.8%
Other values (2) 760
10.4%
ValueCountFrequency (%)
1 476
6.5%
2 581
8.0%
3 660
9.0%
4 756
10.4%
5 420
5.8%
6 431
5.9%
7 372
5.1%
8 388
5.3%
9 824
11.3%
10 869
11.9%
ValueCountFrequency (%)
12 524
7.2%
11 994
13.6%
10 869
11.9%
9 824
11.3%
8 388
 
5.3%
7 372
 
5.1%
6 431
5.9%
5 420
5.8%
4 756
10.4%
3 660
9.0%

Price Range
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing766
Missing (%)10.5%
Memory size7.4 KiB
High Price
5193 
Medium Price
1336 

Length

Max length12
Median length10
Mean length10.409251
Min length10

Characters and Unicode

Total characters67962
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMedium Price
2nd rowMedium Price
3rd rowMedium Price
4th rowMedium Price
5th rowMedium Price

Common Values

ValueCountFrequency (%)
High Price 5193
71.2%
Medium Price 1336
 
18.3%
(Missing) 766
 
10.5%

Length

2023-05-12T12:11:50.385884image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-12T12:11:50.578410image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
price 6529
50.0%
high 5193
39.8%
medium 1336
 
10.2%

Most occurring characters

ValueCountFrequency (%)
i 13058
19.2%
e 7865
11.6%
6529
9.6%
P 6529
9.6%
r 6529
9.6%
c 6529
9.6%
H 5193
 
7.6%
g 5193
 
7.6%
h 5193
 
7.6%
M 1336
 
2.0%
Other values (3) 4008
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 48375
71.2%
Uppercase Letter 13058
 
19.2%
Space Separator 6529
 
9.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 13058
27.0%
e 7865
16.3%
r 6529
13.5%
c 6529
13.5%
g 5193
 
10.7%
h 5193
 
10.7%
d 1336
 
2.8%
u 1336
 
2.8%
m 1336
 
2.8%
Uppercase Letter
ValueCountFrequency (%)
P 6529
50.0%
H 5193
39.8%
M 1336
 
10.2%
Space Separator
ValueCountFrequency (%)
6529
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 61433
90.4%
Common 6529
 
9.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 13058
21.3%
e 7865
12.8%
P 6529
10.6%
r 6529
10.6%
c 6529
10.6%
H 5193
 
8.5%
g 5193
 
8.5%
h 5193
 
8.5%
M 1336
 
2.2%
d 1336
 
2.2%
Other values (2) 2672
 
4.3%
Common
ValueCountFrequency (%)
6529
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 67962
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 13058
19.2%
e 7865
11.6%
6529
9.6%
P 6529
9.6%
r 6529
9.6%
c 6529
9.6%
H 5193
 
7.6%
g 5193
 
7.6%
h 5193
 
7.6%
M 1336
 
2.0%
Other values (3) 4008
 
5.9%

Has_Discount
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size57.1 KiB
0
4857 
1
2438 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7295
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 4857
66.6%
1 2438
33.4%

Length

2023-05-12T12:11:50.714664image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-12T12:11:50.881366image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4857
66.6%
1 2438
33.4%

Most occurring characters

ValueCountFrequency (%)
0 4857
66.6%
1 2438
33.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7295
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4857
66.6%
1 2438
33.4%

Most occurring scripts

ValueCountFrequency (%)
Common 7295
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4857
66.6%
1 2438
33.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7295
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4857
66.6%
1 2438
33.4%

Interactions

2023-05-12T12:11:42.644053image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:26.909108image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:28.870007image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:30.659078image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:32.327204image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:33.955786image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:35.801472image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:37.542374image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:39.218582image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:40.919690image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:42.812438image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:27.240216image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:29.055473image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:30.830819image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:32.496666image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:34.140735image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:36.006356image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:37.716259image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:39.392468image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:41.093587image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:42.994729image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:27.430565image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:29.243799image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:31.027605image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:32.680095image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:34.317039image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:36.197250image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:37.892986image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:39.570603image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:41.268954image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:43.342921image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:27.624266image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:29.421461image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:31.190704image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:32.838924image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:34.483764image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:36.370667image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:38.059875image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:39.740502image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:41.430918image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:43.492103image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:27.804141image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:29.592626image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:31.349411image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:32.986853image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:34.644198image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:36.532443image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:38.224666image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:39.899801image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:41.616246image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:43.656979image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:27.993149image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:29.771333image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:31.514024image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:33.154859image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:34.803109image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:36.700090image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:38.385241image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:40.067707image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:41.825919image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:43.824437image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:28.189406image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:29.957497image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:31.686422image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:33.321341image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:34.977008image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:36.876988image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:38.565138image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:40.246605image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:41.995516image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:43.982365image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:28.372155image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:30.130258image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:31.847562image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:33.479460image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:35.312468image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:37.052256image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:38.730565image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:40.422503image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:42.159297image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:44.148052image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:28.531868image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:30.306667image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:32.010440image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:33.643283image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:35.478659image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:37.220298image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:38.896474image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:40.595404image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:42.328958image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:44.296222image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:28.694471image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:30.479472image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:32.165254image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:33.798013image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:35.631571image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:37.378207image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:39.054667image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:40.752315image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-05-12T12:11:42.484997image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Correlations

2023-05-12T12:11:51.013006image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
NoC/codeItem_codeOrignal_priceCTNPCSTotal_discountDiscount_priceDiscount_per_pieceMonthProduct_namePrice RangeHas_Discount
No1.000-0.0500.0170.5900.0800.030-0.2460.127-0.252-0.0120.1300.2180.366
C/code-0.0501.0000.215-0.195-0.032-0.2390.0650.0370.0810.0350.2080.2960.283
Item_code0.0170.2151.000-0.3790.195-0.1290.4520.5250.484-0.0440.9990.7050.555
Orignal_price0.590-0.195-0.3791.000-0.1270.150-0.552-0.317-0.5830.0280.4560.9370.571
CTN0.080-0.0320.195-0.1271.0000.3530.2500.3480.195-0.0000.0830.0360.145
PCS0.030-0.239-0.1290.1500.3531.0000.1310.5420.035-0.0010.2710.0000.051
Total_discount-0.2460.0650.452-0.5520.2500.1311.0000.5550.984-0.1710.1410.0510.135
Discount_price0.1270.0370.525-0.3170.3480.5420.5551.0000.519-0.0790.2320.3280.461
Discount_per_piece-0.2520.0810.484-0.5830.1950.0350.9840.5191.000-0.1840.2830.5950.757
Month-0.0120.035-0.0440.028-0.000-0.001-0.171-0.079-0.1841.0000.0910.1370.270
Product_name0.1300.2080.9990.4560.0830.2710.1410.2320.2830.0911.0000.7920.563
Price Range0.2180.2960.7050.9370.0360.0000.0510.3280.5950.1370.7921.0000.515
Has_Discount0.3660.2830.5550.5710.1450.0510.1350.4610.7570.2700.5630.5151.000

Missing values

2023-05-12T12:11:44.550025image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-12T12:11:44.962367image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

NoDateC/codeCustomer_nameCustomer_addressItem_codeProduct_nameOrignal_priceCTNPCSTotal_discountDiscount_priceDiscount_per_pieceMonthPrice RangeHas_Discount
032021-09-109295BIG BITE FAST FOODWARSAK ROAD1770DEEN'S CHADDER CHEESE BLOCK 2K14500110100013500.0100.09Medium Price1
132021-09-109295BIG BITE FAST FOODWARSAK ROAD1771DEEN'S MOZRELLA CHEESE BLOCK19250053759250.075.09Medium Price1
242021-09-108992PIZZA FLAMEIBRAHIM MARKET1770DEEN'S CHADDER CHEESE BLOCK 2K14500022002700.0100.09Medium Price1
342021-09-108992PIZZA FLAMEIBRAHIM MARKET1771DEEN'S MOZRELLA CHEESE BLOCK19250021503700.075.09Medium Price1
452021-09-109903CHIL & GRILL 0312.9553655MOHMAD ZAI1770DEEN'S CHADDER CHEESE BLOCK 2K14500022002700.0100.09Medium Price1
552021-09-109903CHIL & GRILL 0312.9553655MOHMAD ZAI1771DEEN'S MOZRELLA CHEESE BLOCK19250021503700.075.09Medium Price1
662021-09-116544CHATRALCAMPUS1758DEEN'S CHEDDAR 200GM201500401240.00.09High Price0
762021-09-116544CHATRALCAMPUS1760DEEN'S MOZARELLA 200GM224250602070.00.09High Price0
862021-09-116544CHATRALCAMPUS1762DEEN'S PIZZA CHEESE 200GM24050020740.00.09High Price0
962021-09-116544CHATRALCAMPUS1763DEEN'S SLICE CHEESE 200GM23600020590.00.09High Price0
NoDateC/codeCustomer_nameCustomer_addressItem_codeProduct_nameOrignal_priceCTNPCSTotal_discountDiscount_priceDiscount_per_pieceMonthPrice RangeHas_Discount
728533392022-12-023483PICK IN SAVEKHYBER SUPER MARKET1784DEENS SLICE 250G281160602556.00.012High Price0
728633402022-12-022063QURESHI G/SSaddar Cantt Peshawar1760DEEN'S MOZARELLA 200GM324720122705142.022.512NaN1
728733412022-12-028907INSAF FAST FOODBILAL MARKET1770DEEN'S CHADDER CHEESE BLOCK 2K225500102255.00.012High Price0
728833412022-12-028907INSAF FAST FOODBILAL MARKET1771DEEN'S MOZRELLA CHEESE BLOCK258500205170.00.012High Price0
728933422022-12-039947OTIMORISALPOOR1770DEEN'S CHADDER CHEESE BLOCK 2K225500306765.00.012High Price0
729033422022-12-039947OTIMORISALPOOR1771DEEN'S MOZRELLA CHEESE BLOCK258500307755.00.012High Price0
729133432022-12-033445FOOD DISTRICTSADAR1771DEEN'S MOZRELLA CHEESE BLOCK258500205170.00.012High Price0
729233432022-12-033445FOOD DISTRICTSADAR1770DEEN'S CHADDER CHEESE BLOCK 2K225500204510.00.012High Price0
729333442022-12-033467IQRA SWEET & BAKERSCAMPUS1784DEENS SLICE 250G28116020852.00.012High Price0
729433452022-12-039031UNIVERSITY BAKERS 03335992712PESHAWAR UNIVERSITY1759DEEN'S CHEDDAR 400GM270720302256.00.012High Price0

Duplicate rows

Most frequently occurring

NoDateC/codeCustomer_nameCustomer_addressItem_codeProduct_nameOrignal_priceCTNPCSTotal_discountDiscount_priceDiscount_per_pieceMonthPrice RangeHas_Discount# duplicates
3988962022-01-032330NAWAZ GENERAL STOREFAQERA ABAD1760DEEN'S MOZARELLA 200GM24635020758.00.01High Price02
574733062022-11-263445FOOD DISTRICTSADAR1771DEEN'S MOZRELLA CHEESE BLOCK258500102585.00.011High Price02
08962022-01-032330FOOD DISTRICTFAQERA ABAD1760DEEN'S MOZARELLA 200GM24635010758.00.01Low Price00
18962022-01-032330FOOD DISTRICTFAQERA ABAD1760DEEN'S MOZARELLA 200GM24635010758.00.01Medium Price00
28962022-01-032330FOOD DISTRICTFAQERA ABAD1760DEEN'S MOZARELLA 200GM24635010758.00.01High Price00
38962022-01-032330FOOD DISTRICTFAQERA ABAD1760DEEN'S MOZARELLA 200GM24635010758.00.011Low Price00
48962022-01-032330FOOD DISTRICTFAQERA ABAD1760DEEN'S MOZARELLA 200GM24635010758.00.011Medium Price00
58962022-01-032330FOOD DISTRICTFAQERA ABAD1760DEEN'S MOZARELLA 200GM24635010758.00.011High Price00
68962022-01-032330FOOD DISTRICTFAQERA ABAD1760DEEN'S MOZARELLA 200GM246350102585.00.01Low Price00
78962022-01-032330FOOD DISTRICTFAQERA ABAD1760DEEN'S MOZARELLA 200GM246350102585.00.01Medium Price00